本文主要是介绍蒙特卡洛电动汽车负荷预测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
部分代码：
P_load=load('load.txt');
P_load=P_load/100000;
B=EV_load(958); %每个节点接入1250辆电动汽车 接入位置8 14 29
for i=1:24
[Ploss,V]=IEEE33(B(i),P_load(i));
Ploss_after(i)=Ploss;
V_after(i,:)=V;
end
for i=1:24
[Ploss,V]=test(P_load(i));
Ploss_before(i)=Ploss;
V_before(i,:)=V;
end
figure(2)
plot( Ploss_before,'-*b');
hold on
plot(Ploss_after,'-or');
title('电动汽车负荷接入IEEE33节点前后网损变化')
legend('接入IEEE33节点前','接入IEEE33节点后')
xlabel('时刻/h');
ylabel('网损/kW');
flage=21; %选择不同时刻的电压值结果分析
figure(3)
plot(V_before(flage,:),'-*b');
hold on
plot(V_after(flage,:),'-or');
title('电动汽车负荷接入IEEE33节点前后电压标幺值变化')
legend('接入IEEE33节点前','接入IEEE33节点后')
xlabel('IEEE33节点序号');
ylabel('电压值（p.u.）');
%%仅展示网络接入负荷后节点电压标幺值立体图
mat=[
1 1.05000000000000 1.04287213168280 1.03169476111295 1.00933052579888 0.986588989940550 0.930099486115464 0.916438721027518 0.895831501848261 0.873414785957754 0.851582632475910 0.848222112820388 0.842037969352419 0.814222862940509 0.802601644026615 0.800214111000742 0.797918584131601 0.794450691868547 0.793431728688100 1.04214248294003 1.03719899791252 1.03622491676297 1.03534319162764 1.02674003550633 1.01750832841635 1.01290362222376 0.925114836418328 0.918338473902728 0.888525212117183 0.866633891958827 0.861169674354207 0.854688745152745 0.853260335876191 0.852817366598989
2 1.05000000000000 1.04327379649932 1.03275041589090 1.01152666533507 0.989930679315199 0.936322185194837 0.923284586007907 0.903519397502969 0.882254659872347 0.861517461585151 0.858318393456601 0.852423306122978 0.825869347967295 0.814751859311421 0.812580142241106 0.810491548012139 0.807339778354964 0.806412814457547 1.04259942412313 1.03803098101468 1.03713085515260 1.03631611684994 1.02817795912955 1.01965981816354 1.01541181115548 0.931623724267564 0.925231563378492 0.897135397068056 0.876487282028976 0.871495580460494 0.865577095209478 0.864272751166238 0.863868276192813
3 1.05000000000000 1.04354561060273 1.03346414591244 1.01304896305934 0.992269848138107 0.940711318275734 0.928145598671718 0.909039667693129 0.888609292605677 0.868673626944631 0.865594397320487 0.859916491132736 0.834329039083645 0.823607515784075 0.821565466087650 0.819601147201922 0.816639244355416 0.815767511099764 1.04290418689065 1.03855925385222 1.03770319786607 1.03692837345643 1.02911925197616 1.02102582506147 1.01699006118021 0.936205993875666 0.930074378614049 0.903138052681494 0.883334001000980 0.878623343035751 0.873040123225188 0.871809742329208 0.871428216894812
4 1.05000000000000 1.04369849201017 1.03386541264842 1.01392291186583 0.993623521332753 0.943266949604992 0.930990521207399 0.912297939150015 0.892363208133327 0.872907419753337 0.869900654268426 0.864355200434629 0.839365207535630 0.828892545502355 0.826916005665145 0.825014438373173 0.822148474711129 0.821304623007772 1.04307341822847
这篇关于蒙特卡洛电动汽车负荷预测的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
